IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i4p1270-d745587.html
   My bibliography  Save this article

Benchmarking Approaches for Assessing the Performance of Building Control Strategies: A Review

Author

Listed:
  • Clara Ceccolini

    (Bosch Thermotechnology GmbH, Junkersstraße 20-24, 73243 Wernau, Germany
    INATECH Department of Sustainable Systems Engineering, Freiburg University, Emmy-Noether-Straße 2, 79110 Freiburg, Germany)

  • Roozbeh Sangi

    (Bosch Thermotechnology GmbH, Junkersstraße 20-24, 73243 Wernau, Germany)

Abstract

In the last few decades, researchers have shown that advanced building controllers can reduce energy consumption without negatively impacting occupants’ wellbeing and help to manage building systems, which are becoming increasingly complex. Nevertheless, the lack of benefit awareness and demonstration projects undermines stakeholders’ trust, justifying the reluctance to approve new controls in the industry. Therefore, it is necessary to support the development of controls through solid arguments testifying to the performance gain that can be achieved. However, the absence of standardized and systematic testing methods limits the generalization of results and the ability to make fair cross-study comparisons. This study presents an overview of the different benchmarking approaches used to assess control performance. Our goal is to highlight trends, limitations, and controversies through analytics to support the definition of best practices, which remains a widely discussed topic in this research area. We aim to focus on simulation-based benchmarking, which is regarded as a promising solution to overcome the time and cost requirements related to field or hardware-in-the-loop testing. We identify and investigate four key steps relating to virtual benchmarking: defining the key performance indicators, specifying the reference control, characterizing the test scenarios, and post-processing the results. This work confirmed the expected heterogeneity, underlined recurrent features with the help of analytics, and recognized limits and open challenges.

Suggested Citation

  • Clara Ceccolini & Roozbeh Sangi, 2022. "Benchmarking Approaches for Assessing the Performance of Building Control Strategies: A Review," Energies, MDPI, vol. 15(4), pages 1-30, February.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1270-:d:745587
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/4/1270/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/4/1270/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Maximilian Hoffmann & Leander Kotzur & Detlef Stolten & Martin Robinius, 2020. "A Review on Time Series Aggregation Methods for Energy System Models," Energies, MDPI, vol. 13(3), pages 1-61, February.
    2. Nägele, Florian & Kasper, Thomas & Girod, Bastien, 2017. "Turning up the heat on obsolete thermostats: A simulation-based comparison of intelligent control approaches for residential heating systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 1254-1268.
    3. Shaikh, Pervez Hameed & Nor, Nursyarizal Bin Mohd & Nallagownden, Perumal & Elamvazuthi, Irraivan & Ibrahim, Taib, 2014. "A review on optimized control systems for building energy and comfort management of smart sustainable buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 409-429.
    4. Yang, Lei & Nagy, Zoltan & Goffin, Philippe & Schlueter, Arno, 2015. "Reinforcement learning for optimal control of low exergy buildings," Applied Energy, Elsevier, vol. 156(C), pages 577-586.
    5. Smarra, Francesco & Jain, Achin & de Rubeis, Tullio & Ambrosini, Dario & D’Innocenzo, Alessandro & Mangharam, Rahul, 2018. "Data-driven model predictive control using random forests for building energy optimization and climate control," Applied Energy, Elsevier, vol. 226(C), pages 1252-1272.
    6. Kuboth, Sebastian & Heberle, Florian & König-Haagen, Andreas & Brüggemann, Dieter, 2019. "Economic model predictive control of combined thermal and electric residential building energy systems," Applied Energy, Elsevier, vol. 240(C), pages 372-385.
    7. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
    8. Enrico Fabrizio & Valentina Monetti, 2015. "Methodologies and Advancements in the Calibration of Building Energy Models," Energies, MDPI, vol. 8(4), pages 1-27, March.
    9. Ye, Yunyang & Chen, Yan & Zhang, Jian & Pang, Zhihong & O’Neill, Zheng & Dong, Bing & Cheng, Hwakong, 2021. "Energy-saving potential evaluation for primary schools with occupant-centric controls," Applied Energy, Elsevier, vol. 293(C).
    10. Wang, Zhe & Hong, Tianzhen, 2020. "Reinforcement learning for building controls: The opportunities and challenges," Applied Energy, Elsevier, vol. 269(C).
    11. Enescu, Diana, 2017. "A review of thermal comfort models and indicators for indoor environments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1353-1379.
    12. Du, Zhimin & Jin, Xinqiao & Fang, Xing & Fan, Bo, 2016. "A dual-benchmark based energy analysis method to evaluate control strategies for building HVAC systems," Applied Energy, Elsevier, vol. 183(C), pages 700-714.
    13. Mossolly, M. & Ghali, K. & Ghaddar, N., 2009. "Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm," Energy, Elsevier, vol. 34(1), pages 58-66.
    14. Bianchini, Gianni & Casini, Marco & Vicino, Antonio & Zarrilli, Donato, 2016. "Demand-response in building heating systems: A Model Predictive Control approach," Applied Energy, Elsevier, vol. 168(C), pages 159-170.
    15. Goyal, Siddharth & Ingley, Herbert A. & Barooah, Prabir, 2013. "Occupancy-based zone-climate control for energy-efficient buildings: Complexity vs. performance," Applied Energy, Elsevier, vol. 106(C), pages 209-221.
    16. Edorta Carrascal & Izaskun Garrido & Aitor J. Garrido & José María Sala, 2016. "Optimization of the Heating System Use in Aged Public Buildings via Model Predictive Control," Energies, MDPI, vol. 9(4), pages 1-20, March.
    17. Du, Yan & Zandi, Helia & Kotevska, Olivera & Kurte, Kuldeep & Munk, Jeffery & Amasyali, Kadir & Mckee, Evan & Li, Fangxing, 2021. "Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning," Applied Energy, Elsevier, vol. 281(C).
    18. Beckman, William A. & Broman, Lars & Fiksel, Alex & Klein, Sanford A. & Lindberg, Eva & Schuler, Mattias & Thornton, Jeff, 1994. "TRNSYS The most complete solar energy system modeling and simulation software," Renewable Energy, Elsevier, vol. 5(1), pages 486-488.
    19. Salpakari, Jyri & Lund, Peter, 2016. "Optimal and rule-based control strategies for energy flexibility in buildings with PV," Applied Energy, Elsevier, vol. 161(C), pages 425-436.
    20. Fischer, David & Bernhardt, Josef & Madani, Hatef & Wittwer, Christof, 2017. "Comparison of control approaches for variable speed air source heat pumps considering time variable electricity prices and PV," Applied Energy, Elsevier, vol. 204(C), pages 93-105.
    21. Li, Xiwang & Malkawi, Ali, 2016. "Multi-objective optimization for thermal mass model predictive control in small and medium size commercial buildings under summer weather conditions," Energy, Elsevier, vol. 112(C), pages 1194-1206.
    22. Dounis, A.I. & Caraiscos, C., 2009. "Advanced control systems engineering for energy and comfort management in a building environment--A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1246-1261, August.
    23. Du, Zhimin & Jin, Xinqiao & Fan, Bo, 2015. "Evaluation of operation and control in HVAC (heating, ventilation and air conditioning) system using exergy analysis method," Energy, Elsevier, vol. 89(C), pages 372-381.
    24. Afroz, Zakia & Shafiullah, GM & Urmee, Tania & Higgins, Gary, 2018. "Modeling techniques used in building HVAC control systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 83(C), pages 64-84.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhan, Sicheng & Chong, Adrian, 2021. "Data requirements and performance evaluation of model predictive control in buildings: A modeling perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    2. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
    3. Lei, Yue & Zhan, Sicheng & Ono, Eikichi & Peng, Yuzhen & Zhang, Zhiang & Hasama, Takamasa & Chong, Adrian, 2022. "A practical deep reinforcement learning framework for multivariate occupant-centric control in buildings," Applied Energy, Elsevier, vol. 324(C).
    4. Svetozarevic, B. & Baumann, C. & Muntwiler, S. & Di Natale, L. & Zeilinger, M.N. & Heer, P., 2022. "Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: Simulations and experiments," Applied Energy, Elsevier, vol. 307(C).
    5. Coraci, Davide & Brandi, Silvio & Hong, Tianzhen & Capozzoli, Alfonso, 2023. "Online transfer learning strategy for enhancing the scalability and deployment of deep reinforcement learning control in smart buildings," Applied Energy, Elsevier, vol. 333(C).
    6. Silvestri, Alberto & Coraci, Davide & Brandi, Silvio & Capozzoli, Alfonso & Borkowski, Esther & Köhler, Johannes & Wu, Duan & Zeilinger, Melanie N. & Schlueter, Arno, 2024. "Real building implementation of a deep reinforcement learning controller to enhance energy efficiency and indoor temperature control," Applied Energy, Elsevier, vol. 368(C).
    7. Wenquan Jin & Israr Ullah & Shabir Ahmad & Dohyeun Kim, 2019. "Occupant Comfort Management Based on Energy Optimization Using an Environment Prediction Model in Smart Homes," Sustainability, MDPI, vol. 11(4), pages 1-18, February.
    8. Yang, Shiyu & Wan, Man Pun & Chen, Wanyu & Ng, Bing Feng & Dubey, Swapnil, 2021. "Experiment study of machine-learning-based approximate model predictive control for energy-efficient building control," Applied Energy, Elsevier, vol. 288(C).
    9. Pinto, Giuseppe & Deltetto, Davide & Capozzoli, Alfonso, 2021. "Data-driven district energy management with surrogate models and deep reinforcement learning," Applied Energy, Elsevier, vol. 304(C).
    10. O’Dwyer, Edward & Pan, Indranil & Acha, Salvador & Shah, Nilay, 2019. "Smart energy systems for sustainable smart cities: Current developments, trends and future directions," Applied Energy, Elsevier, vol. 237(C), pages 581-597.
    11. Yang, Shiyu & Wan, Man Pun, 2022. "Machine-learning-based model predictive control with instantaneous linearization – A case study on an air-conditioning and mechanical ventilation system," Applied Energy, Elsevier, vol. 306(PB).
    12. Afroz, Zakia & Shafiullah, GM & Urmee, Tania & Higgins, Gary, 2018. "Modeling techniques used in building HVAC control systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 83(C), pages 64-84.
    13. Raman, Naren Srivaths & Devaprasad, Karthikeya & Chen, Bo & Ingley, Herbert A. & Barooah, Prabir, 2020. "Model predictive control for energy-efficient HVAC operation with humidity and latent heat considerations," Applied Energy, Elsevier, vol. 279(C).
    14. Halhoul Merabet, Ghezlane & Essaaidi, Mohamed & Ben Haddou, Mohamed & Qolomany, Basheer & Qadir, Junaid & Anan, Muhammad & Al-Fuqaha, Ala & Abid, Mohamed Riduan & Benhaddou, Driss, 2021. "Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    15. Dengiz, Thomas & Jochem, Patrick & Fichtner, Wolf, 2019. "Demand response with heuristic control strategies for modulating heat pumps," Applied Energy, Elsevier, vol. 238(C), pages 1346-1360.
    16. Park, June Young & Nagy, Zoltan, 2018. "Comprehensive analysis of the relationship between thermal comfort and building control research - A data-driven literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2664-2679.
    17. da Fonseca, André L.A. & Chvatal, Karin M.S. & Fernandes, Ricardo A.S., 2021. "Thermal comfort maintenance in demand response programs: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    18. Sebastian Kuboth & Theresa Weith & Florian Heberle & Matthias Welzl & Dieter Brüggemann, 2020. "Experimental Long-Term Investigation of Model Predictive Heat Pump Control in Residential Buildings with Photovoltaic Power Generation," Energies, MDPI, vol. 13(22), pages 1-17, November.
    19. Han, Gwangwoo & Joo, Hong-Jin & Lim, Hee-Won & An, Young-Sub & Lee, Wang-Je & Lee, Kyoung-Ho, 2023. "Data-driven heat pump operation strategy using rainbow deep reinforcement learning for significant reduction of electricity cost," Energy, Elsevier, vol. 270(C).
    20. Paulína Šujanová & Monika Rychtáriková & Tiago Sotto Mayor & Affan Hyder, 2019. "A Healthy, Energy-Efficient and Comfortable Indoor Environment, a Review," Energies, MDPI, vol. 12(8), pages 1-37, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1270-:d:745587. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.